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Petri Net based modeling and analysis for improved resource utilization in cloud computing.

ABSTRACT: The cloud is a shared pool of systems that provides multiple resources through the Internet, users can access a lot of computing power using their computer. However, with the strong migration rate of multiple applications towards the cloud, more disks and servers are required to store huge data. Most of the cloud storage service providers are replicating full copies of data over multiple data centers to ensure data availability. Further, the replication is not only a costly process but also a wastage of energy resources. Furthermore, erasure codes reduce the storage cost by splitting data in n chunks and storing these chunks into n + k different data centers, to tolerate k failures. Moreover, it also needs extra computation cost to regenerate the data object. Cache-A Replica On Modification (CAROM) is a hybrid file system that gets combined benefits from both the replication and erasure codes to reduce access latency and bandwidth consumption. However, in the literature, no formal analysis of CAROM is available which can validate its performance. To address this issue, this research firstly presents a colored Petri net based formal model of CAROM. The research proceeds by presenting a formal analysis and simulation to validate the performance of the proposed system. This paper contributes towards the utilization of resources in clouds by presenting a comprehensive formal analysis of CAROM.

SUBMITTER: Rizwan Ali M

PROVIDER: S-EPMC7959626 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundStructural analysis of biochemical networks is a growing field in bioinformatics and systems biology. The availability of an increasing amount of biological data from molecular biological networks promises a deeper understanding but confronts researchers with the problem of combinatorial explosion. The amount of qualitative network data is growing much faster than the amount of quantitative data, such as enzyme kinetics. In many cases it is even impossible to measure quantitative data because of limitations of experimental methods, or for ethical reasons. Thus, a huge amount of qualitative data, such as interaction data, is available, but it was not sufficiently used for modeling purposes, until now. New approaches have been developed, but the complexity of data often limits the application of many of the methods. Biochemical Petri nets make it possible to explore static and dynamic qualitative system properties. One Petri net approach is model validation based on the computation of the system's invariant properties, focusing on t-invariants. T-invariants correspond to subnetworks, which describe the basic system behavior.With increasing system complexity, the basic behavior can only be expressed by a huge number of t-invariants. According to our validation criteria for biochemical Petri nets, the necessary verification of the biological meaning, by interpreting each subnetwork (t-invariant) manually, is not possible anymore. Thus, an automated, biologically meaningful classification would be helpful in analyzing t-invariants, and supporting the understanding of the basic behavior of the considered biological system.MethodsHere, we introduce a new approach to automatically classify t-invariants to cope with network complexity. We apply clustering techniques such as UPGMA, Complete Linkage, Single Linkage, and Neighbor Joining in combination with different distance measures to get biologically meaningful clusters (t-clusters), which can be interpreted as modules. To find the optimal number of t-clusters to consider for interpretation, the cluster validity measure, Silhouette Width, is applied.ResultsWe considered two different case studies as examples: a small signal transduction pathway (pheromone response pathway in Saccharomyces cerevisiae) and a medium-sized gene regulatory network (gene regulation of Duchenne muscular dystrophy). We automatically classified the t-invariants into functionally distinct t-clusters, which could be interpreted biologically as functional modules in the network. We found differences in the suitability of the various distance measures as well as the clustering methods. In terms of a biologically meaningful classification of t-invariants, the best results are obtained using the Tanimoto distance measure. Considering clustering methods, the obtained results suggest that UPGMA and Complete Linkage are suitable for clustering t-invariants with respect to the biological interpretability.ConclusionWe propose a new approach for the biological classification of Petri net t-invariants based on cluster analysis. Due to the biologically meaningful data reduction and structuring of network processes, large sets of t-invariants can be evaluated, allowing for model validation of qualitative biochemical Petri nets. This approach can also be applied to elementary mode analysis.

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Petri Net based modeling and analysis for improved resource utilization in cloud computing.

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